A media content recommendations engine identifies media content that may be of interest to a potential consumer of the media content, such a user of a media service. The media content recommendation engine may utilize any of a number of conventional media content recommendation technologies to identify, based on attributes of the media content and information about the user, media content that may be of interest to the user. For example, the media content recommendations engine may use collaborative filtering, matrix factorization, cosine similarity in a vector space model, or other conventional media content recommendation technologies to identify media content that may be of interest to the user.
Such conventional media content recommendation technologies have limitations and leave room for improvement. As an example, the quality of media content recommendations that are identified using certain conventional media content recommendation technologies is highly dependent on the quality of the data set to which the technologies are applied. However, curation of a quality data set, such as a quality vector space model data set that contains information useful for generating media content recommendations, is arduous for a media service provider. As another example, conventional media content recommendation technologies are limited in the information that can be used to identify media content recommendations, how the recommendations are identified from the information, the types of recommendations that can be identified from the information, and/or the information that can be provided to a user together with the media content recommendations.